Traveling Salesman Problem Using an Enhanced Hybrid Swarm Optimization Algorithm

The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.

Development of hybrid optimization algorithm for structures furnished with seismic damper devices using the particle swarm optimization method and gravitational search algorithm

Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom structures.The current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different dampers and isolators that can be used.Furthermore,there is a lack of studies regarding the optimum location for various viscous and wall dampers.The main aim of this study is hybridization of the particle swarm optimization(PSO) and gravitational search algorithm(GSA) to optimize the performance of earthquake energy dissipation systems(i.e.,damper devices) simultaneously with optimizing the characteristics of the structure.Four types of structural dampers device are considered in this study:(ⅰ) variable stiffness bracing(VSB) system,(ⅱ) rubber wall damper(RWD),(ⅲ) nonlinear conical spring bracing(NCSB) device,(iv) and multi-action stiffener(MAS) device.Since many parameters may affect the design of seismic resistant structures,this study proposes a hybrid of PSO and GSA to develop a hybrid,multi-objective optimization method to resolve the aforementioned problems.The characteristics of the above-mentioned damper devices as well as the section size for structural beams and columns are considered as variables for development of the PSO-GSA optimization algorithm to minimize structural seismic response in terms of nodal displacement(in three directions) as well as plastic hinge formation in structural members simultaneously with the weight of the structure.After that,the optimization algorithm is implemented to identify the best position of the damper device in the structural frame to have the maximum effect and minimize the seismic structure response.To examine the performance of the proposed PSO-GSA optimization method,it has been applied to a three-story reinforced structure equipped with a seismic damper device.The results revealed that the method successfully optimized the earthquake energy dissipation systems and reduced the effects of earthquakes on structures,which significantly increase the building’s stability and safety during seismic excitation.The analysis results showed a reduction in the seismic response of the structure regarding the formation of plastic hinges in structural members as well as the displacement of each story to approximately 99.63%,60.5%,79.13% and 57.42% for the VSB device,RWD,NCSB device,and MAS device,respectively.This shows that using the PSO-GSA optimization algorithm and optimized damper devices in the structure resulted in no structural damage due to earthquake vibration.

Optimization of Thermal Aware VLSI Non-Slicing Floorplanning Using Hybrid Particle Swarm Optimization Algorithm-Harmony Search Algorithm

Floorplanning is a prominent area in the Very Large-Scale Integrated (VLSI) circuit design automation, because it influences the performance, size, yield and reliability of the VLSI chips. It is the process of estimating the positions and shapes of the modules. A high packing density, small feature size and high clock frequency make the Integrated Circuit (IC) to dissipate large amount of heat. So, in this paper, a methodology is presented to distribute the temperature of the module on the layout while simultaneously optimizing the total area and wirelength by using a hybrid Particle Swarm Optimization-Harmony Search (HPSOHS) algorithm. This hybrid algorithm employs diversification technique (PSO) to obtain global optima and intensification strategy (HS) to achieve the best solution at the local level and Modified Corner List algorithm (MCL) for floorplan representation. A thermal modelling tool called hotspot tool is integrated with the proposed algorithm to obtain the temperature at the block level. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solution.

Robot stereo vision calibration method with genetic algorithm and particle swarm optimization

Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.

Hybrid anti-prematuration optimization algorithm

Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem.

A New Metaheuristic Approach to Solving Benchmark Problems: Hybrid Salp Swarm Jaya Algorithm

Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational cost.As with other types of algorithms,in metaheuristic algorithms,one of the methods used to improve performance and achieve results closer to the target result is the hybridization of algorithms.In this study,a hybrid algorithm(HSSJAYA)consisting of salp swarm algorithm(SSA)and jaya algorithm(JAYA)is designed.The speed of achieving the global optimum of SSA,its simplicity,easy hybridization and JAYA’s success in achieving the best solution have given us the idea of creating a powerful hybrid algorithm from these two algorithms.The hybrid algorithm is based on SSA’s leader and follower salp system and JAYA’s best and worst solution part.HSSJAYA works according to the best and worst food source positions.In this way,it is thought that the leader-follower salps will find the best solution to reach the food source.The hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark functions.The results were compared with SSA,JAYA,cuckoo search algorithm(CS),firefly algorithm(FFA)and genetic algorithm(GA).As a result,a hybrid algorithm that provided results closer to the desired fitness value in benchmark functions was obtained.In addition,these results were statistically compared using wilcoxon rank sum test with other algorithms.According to the statistical results obtained from the results of the benchmark functions,it was determined that HSSJAYA creates a statistically significant difference in most of the problems compared to other algorithms.

Application of several optimization techniques for estimating TBM advance rate in granitic rocks

This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.

A Hybrid Algorithm Based on PSO and GA for Feature Selection

One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection of features has an essential importance in the classification process to be able minimize computational time,which decreases data size and increases the precision and effectiveness of specific machine learning activities.Due to its superiority to conventional optimization methods,several metaheuristics have been used to resolve FS issues.This is why hybrid metaheuristics help increase the search and convergence rate of the critical algorithms.A modern hybrid selection algorithm combining the two algorithms;the genetic algorithm(GA)and the Particle Swarm Optimization(PSO)to enhance search capabilities is developed in this paper.The efficacy of our proposed method is illustrated in a series of simulation phases,using the UCI learning array as a benchmark dataset.

Stroke optimization of a novel multi-station rotary polishing robot based on workspace analysis

In order to meet the polishing requirement of faucets and other products,a novel multi-station rotary polishing robot is designed,which is a PPPR + RR type of degree of freedom( DOF) distribution structure,and is similar to dual-arm robot. Forward and inverse kinematic analysis is carried out by robot modeling. In order to make this robot structure more compact,first of all,X,Y and Z three moving degrees of freedom( DOF) limit stroke polishing need is calculated by using an artificial fish swarm algorithm,which analyzes dexterous workspace of this robot. Then,on the basis of the above analysis,the three DOF stroke is optimized. Simulation and polishing experimental results verify that this polishing robot with optimized stroke parameters can meet the polishing needs of faucets and other bathroom pieces.

Application of Metaheuristic Algorithms for Optimizing Longitudinal Square Porous Fins

The objectives of this study involve the optimization of longitudinal porous fins of square cross-section using metaheuristic algorithms.A generalized nonlinear ordinary differential equation is derived using Darcy and Fourier’s laws in the energy balance around a control volume and is solved numerically using RFK 45 method.The temperature of the base surface is higher than the fin surface,and the fin tip is kept adiabatic or cooled by convection heat transfer.The other pertinent parameters include Rayleigh number(100≤Ra≤10^(4)),Darcy number,(10^(−4)≤Da≤10^(−2)),relative thermal conductivity ratio of solid phase to fluid(1000≤kr≤8000),Nusselt number(10≤Nu≤100),porosity(0.1≤φ≤0.9).The impacts of these parameters on the entropy generation rate are investigated and optimized using metaheuristic algorithms.In computer science,metaheuristic algorithms are one of the most widely used techniques for optimization problems.In this research,three metaheuristic algorithms,including the firefly algorithm(FFA),particle swarm algorithm(PSO),and hybrid algorithm(FFAPSO)are employed to examine the performance of square fins.It is demonstrated that FFA-PSO takes fewer iterations and less computational time to converge compared to other algorithms.

A Novel Approach Based on Hybrid Algorithm for Energy Efficient Cluster Head Identification in Wireless Sensor Networks

The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission.The clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster head.The major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor Network.The proposed model is a hybridization of Glowworm Swarm Optimization(GSO)and Artificial Bee Colony(ABC)algorithm for the better identification of cluster head.The performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35%better value and reduction of time complexity upto 1.46%.Above all,the proposed model is 16%ahead of alive node count when compared with the existing methodologies.

具有紧时、高能耗特征的混合流水车间多目标调度优化问题

针对具有紧时、高能耗工序特征的混合流水车间调度问题,以优化产品暴露时间、最大完工时间和能源消耗为目标,建立混合流水车间调度模型,并提出一种改进的多目标粒子群算法进行有效求解。首先构建了基于ISDE指标的档案维护策略及局部邻域搜索策略,辅助算法跃出局部极值及减少生产阻塞。之后,提出一种基于模糊理论的决策分析方法选取最优调度方案。最后,通过仿真实验验证提出的多目标调度模型与算法的可行性和优越性。

考虑能耗和运输的有限缓冲区混合流水车间调度

为解决生产调度不及时、加工过程中能耗过大等问题,构建了有限缓冲区混合流水车间调度优化模型。模型以最小化最大完工时间和车间总能耗为目标,将运输时间、广义能耗和缓冲区容量等资源限制作为约束,并应用开关机节能策略减少待机能耗,验证了优化模型的可行性;设计一种狮群算法,采用随机产生与贪婪选择相结合的种群初始化方法,提高初始解质量和求解效率,验证了狮群算法的优越性。实验结果表明:该算法能有效解决考虑能耗和运输时间的有限缓冲区混合流水车间调度问题,优化模型能依照实际需要进行柔性调节,达到制造型企业合理排产、节能减排的目的。

考虑系统稳定边界的同步调相机励磁与升压变参数联合优化

现有提升调相机动态无功特性的参数优化方法侧重于电磁参数的优化,这给生产企业带来较高的工艺要求和较大的成本压力。针对该问题提出考虑系统稳定约束的调相机励磁系统及升压变参数联合优化方法,分析其对电磁参数优化的可替代性。首先,推导了基于Park模型下调相机的无功频域特性,与6阶实用模型下的无功频域特性对比,基于调相机的Park模型可提升调相机动态无功特性的分析精度。然后,提出根据调相机并网系统的稳定边界确定参数的优化区间,采用频域灵敏度方法确定重点参数,并基于人工鱼群算法进行参数优化。最后,通过仿真结果表明,励磁系统与升压变参数的联合优化,可获得与仅优化电磁参数时相近的调相机动态无功性能,验证了电磁参数优化的可替代性,从而降低调相机的制造成本,扩大同步调相机的应用场合和范围。

基于混合粒子群算法的波浪能发电集群优化方法

对波浪能发电集群的优化控制有助于波浪能的有效利用,为此文章提出了基于混合粒子群算法的波浪能发电集群优化方法。以直驱式发电装置为研究对象,探讨其构成发电集群短期尺度下稳定状态的数学模型,由简至繁依次考虑波浪动态压力、装置间辐射影响和遮挡效应,以便更准确地模拟一定密集度的波浪能发电装置部署下的实际效果。以集群功率最大化为优化目标,根据装置运动和海域能量约束,提出混合粒子群算法求解集群的最优参数,在传统算法基础上设定自适应惯性权重并加入交叉和变异操作,以应对复杂集群方程解空间的多峰性问题。算例结果验证了所述集群优化方法的有效性,求解质量良好;同时表明波浪能发电集群规模越大,装置之间的辐射影响越复杂,遮挡效应越明显。

基于人工鱼群-遗传算法的多品种小批量零件数控加工工艺优化研究

基于多品种小批量零件加工成本高的问题,基于人工鱼群-遗传算法(AFSA-GA)构建了数控机床能耗模型,以实现零件加工能耗下降。首先,将数控机床功率划分为各工序功率模型,基于功率模型与工作时间关系得出机床运转能耗模型,结合产品表面粗糙度模型,对各工序能耗模型及整体粗糙度进行归一化处理,形成整体能耗模型;其次,以能耗及粗糙度为目标函数,建立AFSA-GA算法,通过对各工序能耗求解得出最适当的机床功率及其所对应的能耗和表面粗糙度;最后,针对所获得的最优功率,进行优化结果的验证,为五轴机床的实际加工提供解决方案。

无人机17kW电机振动噪声分析与巡航转速下尖端噪声优化

随着无人机的迅速发展,噪声问题影响消费者体验及AI交互、语音识别等技术,限制了无人机应用潜力。该文针对一台17 kW无人机用外转子永磁同步电机进行研究。为降低电机尖端振动噪声,且保留原电机电磁性能,重点提出优化磁极和定子开槽的方法。具体以平均转矩、转矩脉动等作为约束条件,构建多目标优化数学模型,并利用混合粒子群优化算法求解。该文深入探讨磁极参数、定子开槽对低阶次径向气隙磁通密度空间谐波特征的影响。并对电机转子模态仿真,以研究径向电磁力与空间模态的作用机理。在多转速情况下,以巡航转速为重点,分析整体电机电磁振动噪声特征。最后,仿真和实验结果表明,电机在巡航转速下的尖端噪声显著减小。验证了优化结构对无人机电机尖端振动噪声有明显抑制作用,对解决无人机噪声问题具有重要意义。

Analytical Hybrid Particle Swarm Optimization Algorithm for Optimal Siting and Sizing of Distributed Generation in Smart Grid

In this paper,the hybridization of standard particle swarm optimisation(PSO)with the analytical method(2/3 rd rule)is proposed,which is called as analytical hybrid PSO(AHPSO)algorithm used for the optimal siting and sizing of distribution generation.The proposed AHPSO algorithm is implemented to cater for uniformly distributed,increasingly distributed,centrally distributed,and randomly distributed loads in conventional power systems.To demonstrate the effectiveness of the proposed algorithm,the convergence speed and optimization performances of standard PSO and the proposed AHPSO algorithms are compared for two cases.In the first case,the performances of both the algorithms are compared for four different load distributions via an IEEE 10-bus system.In the second case,the performances of both the algorithms are compared for IEEE 10-bus,IEEE 33-bus,IEEE 69-bus systems,and a real distribution system of Korea.Simulation results show that the proposed AHPSO algorithm converges significantly faster than the standard PSO.The results of the proposed algorithm are compared with those of an analytical algorithm,and the results of them are similar.

A hybrid constriction coefficientbased particle swarm optimization and gravitational search algorithm for training multi-layer perceptron

Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPSOGSA)has been employed for training MLP to overcome sensitivity to initialization,premature convergence,and stagnation in local optima problems of MLP.Design/methodology/approach-In this study,the exploration of the search space is carried out by gravitational search algorithm(GSA)and optimization of candidate solutions,i.e.exploitation is performed by particle swarm optimization(PSO).For training the multi-layer perceptron(MLP),CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error.Secondly,a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.Findings-The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems.Besides,it gives the best results for breast cancer,heart,sine function and sigmoid function datasets as compared to other participating algorithms.Moreover,CPSOGSA also provides very competitive results for other datasets.Originality/value-The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP.Basically,CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power.In the research literature,a little work is available where CPSO and GSA have been utilized for training MLP.The only related research paper was given by Mirjalili et al.,in 2012.They have used standard PSO and GSA for training simple FNNs.However,the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms.In this paper,eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs.In addition,a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5%significance level to statistically validate the simulation results.Besides,eight state-of-the-art metaheuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm

Over recent decades,the artificial neural networks(ANNs)have been applied as an effective approach for detecting damage in construction materials.However,to achieve a superior result of defect identification,they have to overcome some shortcomings,for instance slow convergence or stagnancy in local minima.Therefore,optimization algorithms with a global search ability are used to enhance ANNs,i.e.to increase the rate of convergence and to reach a global minimum.This paper introduces a two-stage approach for failure identification in a steel beam.In the first step,the presence of defects and their positions are identified by modal indices.In the second step,a feedforward neural network,improved by a hybrid particle swarm optimization and gravitational search algorithm,namely FNN-PSOGSA,is used to quantify the severity of damage.Finite element(FE)models of the beam for two damage scenarios are used to certify the accuracy and reliability of the proposed method.For comparison,a traditional ANN is also used to estimate the severity of the damage.The obtained results prove that the proposed approach can be used effectively for damage detection and quantification.